Social Media's Initial Reaction to Information and Misinformation on Ebola, August 2014: Facts and Rumors

Abstract

OBJECTIVE: We analyzed misinformation about Ebola circulating on Twitter and Sina Weibo, the leading Chinese microblog platform, at the outset of the global response to the 2014-2015 Ebola epidemic to help public health agencies develop their social media communication strategies. METHODS: We retrieved Twitter and Sina Weibo data created within 24 hours of the World Health Organization announcement of a Public Health Emergency of International Concern (Batch 1 from August 8, 2014, 06:50:00 Greenwich Mean Time [GMT] to August 9, 2014, 06:49:59 GMT) and seven days later (Batch 2 from August 15, 2014, 06:50:00 GMT to August 16, 2014, 06:49:59 GMT). We obtained and analyzed a 1% random sample of tweets containing the keyword Ebola. We retrieved all Sina Weibo posts with Chinese keywords for Ebola for analysis. We analyzed changes in frequencies of keywords, hashtags, and Web links using relative risk (RR) and c(2) feature selection algorithm. We identified misinformation by manual coding and categorizing randomly selected sub-datasets. RESULTS: We identified two speculative treatments (i.e., bathing in or drinking saltwater and ingestion of Nano Silver, an experimental drug) in our analysis of changes in frequencies of keywords and hashtags. Saltwater was speculated to be protective against Ebola in Batch 1 tweets but their mentions decreased in Batch 2 (RR=0.11 for "salt" and RR=0.14 for "water"). Nano Silver mentions were higher in Batch 2 than in Batch 1 (RR=10.5). In our manually coded samples, Ebola-related misinformation constituted about 2% of Twitter and Sina Weibo content. A range of 36%-58% of the posts were news about the Ebola outbreak and 19%-24% of the posts were health information and responses to misinformation in both batches. In Batch 2, 43% of Chinese microblogs focused on the Chinese government sending medical assistance to Guinea. CONCLUSION: Misinformation about Ebola was circulated at a very low level globally in social media in either batch. Qualitative and quantitative analyses of social media posts can provide relevant information to public health agencies during emergency responses.